Activating and inhibiting connections in biological network dynamics.
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However, knowledge of network topology does not allow one to predict network dynamical behavior--for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos.Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon) Xia (nominated by Mark Gerstein).For the full reviews, please go to the Reviewers' comments section.
Affiliation: Department of Physics, University of Colorado, 390 UCB, Boulder, CO 80309, USA. daniel.mcdonald@colorado.edu
ABSTRACT
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Background: Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior--for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results: Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion: The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers: Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon) Xia (nominated by Mark Gerstein). For the full reviews, please go to the Reviewers' comments section. |
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Mentions: For a wide range of conditions, the activating fraction a is strongly correlated with the probability that the dynamics of a random network reach steady state. For a near 1, nearly all runs of the network dynamics reach steady state, while for a near 0, few runs reach steady state; the network dynamics typically oscillate (fig. 2). When a = 0, ~0.01% to 10% of runs reach steady state, depending on network degree. The probability of reaching steady state is only weakly dependent on the size of the network, if the number of connections per node is fixed (fig. 2). Altering the typical magnitude of connection strengths has little effect on the dynamics, because the dynamics are highly saturated (see Methods). |
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Affiliation: Department of Physics, University of Colorado, 390 UCB, Boulder, CO 80309, USA. daniel.mcdonald@colorado.edu
Background: Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior--for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos.
Results: Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate.
Conclusion: The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks.
Reviewers: Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon) Xia (nominated by Mark Gerstein). For the full reviews, please go to the Reviewers' comments section.